#%%
import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):

        super(Net, self).__init__()
        # 定义网络
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)

        self.fc1 = nn.Linear(16*5*5, 120)
        self.fc2 = nn.Linear(120,84)
        self.fc3 = nn.Linear(84, 10)
        return

    def forward(self, x):
        
        # 正向传播
        x = F.max_pool2d(F.relu(self.conv1(x)),(2,2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)

        # 平坦展成一维
        x = x.view(-1, self.num_flat_features(x))

        x= F.relu(self.fc1(x))
        x= F.relu(self.fc2(x))
        x = self.fc3(x)

        return x

    def num_flat_features(self, x):

        size = x.size()[1:]  # 除去批处理维度的其他所有维度
        num_features = 1
        for s in size:
            num_features *= s

        return num_features

net = Net()
print(net)


#%%
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)



#%%
params = list(net.parameters())
print(len(params))
print(params[0].size())  # conv1's .weight


#%%
net.zero_grad()
out.backward(torch.randn(1, 10))


#%%
output = net(input)
target = torch.randn(10)  # 本例子中使用模拟数据
# target = torch.randn(10)  # 本例子中使用模拟数据
print("target is ",target)
# target = torch.randn(10)  # 本例子中使用模拟数据
target = target.view(1, -1)  # 使目标值与数据值形状一致
print("target is ",target)
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)

#%%
print(loss.grad_fn)  # MSELoss
print(loss.grad_fn.next_functions[0][0])  # Linear
print(loss.grad_fn.next_functions[0][0].next_functions[0][0])  # ReLU
#%%
net.zero_grad()     # 清零所有参数（parameter）的梯度缓存

print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)

loss.backward()

print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)


#%%
import torch.optim as optim# 创建优化器（optimizer）
optimizer = optim.SGD(net.parameters(), lr=0.01)

# 在训练的迭代中：

# before
print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)
print('conv1.weight.grad before backward')
print(net.conv1.weight.grad)

optimizer.zero_grad()   # 清零梯度缓存
output = net(input)
loss = criterion(output, target)

# before
print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)
print('conv1.weight.grad before backward')
print(net.conv1.weight.grad)

loss.backward()
optimizer.step()    # 更新参数
